Commodity Futures Trading

Notionally Funding a Trading Systems or Managed Futures Account

When an investor looks at the performance of various trading systems or managed futures accounts, one of the most significant statistics is what the required minimum account size is. It makes no sense considering trading systems, or managed futures accounts that have $100,000 minimums if the investor only has $50,000 to invest.

However, it is valuable to know that frequently the investor can start with less than the minimum through notional funding. For example, an investor could notionally fund a managed futures account or trading systems account at the $50,000 level but tell the manager to trade at a nominal $100,000 level. In other words, the account will trade as though there were $100,000 in it, even though there is not. The investor is simply making use of added leverage.

In the previous example, this means that the account will be trading at 2-to-1 leverage. Meaning the investors will have gains and losses at twice the level. Had the investor only put up a third of the nominal amount minimum then he would see gains and losses at 3 times the level and so on.

Why those using Trading Systems or Managed Futures Accounts Might Want to Consider Notional Funding

Notional funding can be an efficient use of capital, because frequently a trading system or managed futures account will not come anywhere close to using all the money in the account. For example, in Hoffman Asset Management’s case we have a margin-to-equity ratio of generally less than 10%. What this means is that for every $100,000 invested, generally speaking, we will be using less than $10,000 at any given time for margin. The remaining $90,000 sits on the sidelines stagnant. Although it is true that interest on those unused funds can be earned, most investor’s feel they could do better investing those funds elsewhere. Often time’s high net worth individuals or institutions will even put NOTHING in their accounts and trade 100% notionally. The question for investors should be “how can I calculate a reasonable notional level to invest at”.

We feel the answer to that question is one that can be computed based on several statistics. Specifically, what is the maximum drawdown expected and what is the maximum margin that might be needed. For example, Hoffman Asset Management (as of this writing) has had a maximum drawdown of about 17% on a $125,000 nominal account size. This means a $21,250 drawdown in cash terms. The maximum margin usage is about 15% on $125,000 or, about $18,750 in cash terms.

To compute a notional investment amount, we suggest that an investor add the maximum expected drawdown and the maximum expected margin usage. This figure would give the investor the absolute minimum they could invest in the account without having a margin call.

In the previous example, if an investor had started on the worst possible day, and had a $21,250 drawdown, and simultaneously had the maximum margin usage of $18,750, he would have needed a $40,000 of cash in the account to fund that $125,000 nominal account size. Once again, some institutions and individuals who are not worried about margin calls may even decide to fund the account with less than that (or zero).

Benefits of Notional Funding to the Trading Systems or Managed Futures Account Investor

This allows for the smaller, but more aggressive investor to participate in the program without needing to tie up the entire amount in cash. This will amplify their gains and losses at the added leverage level they are using. If, for example, the manager made a 30% return with a 17% drawdown, then the investor at 2-to-1 leverage would have experienced 60% gains with a 34% drawdown.

Once again, this is a more aggressive approach, and we recommend this only for investors who fully understand the benefits and risks of notional funding, but for the right investor, this can be a valuable tool to have in his or her arsenal.

Dean HoffmanHoffman Asset Management

Commodity trading carries significant risks and is not suitable for all investors. Past results are not necessarily indicative of future results

TAGS: Managed Futures Accounts, Managed Futures Account, managed futures

With computers as powerful as they are today, it is easy optimize a trading system to make it look exceptional, but an optimized system is not always a reliable one. Just because a trader can program a computer to have 20/20 hindsight does not mean that the programs future performance will be anything like its past.

The primary problem with optimizing a computer's past performance is that all markets change. A low-volatility market may become a high-volatility market. A market that is prone to trends may become choppy and directionless, and a market that previously had high leverage can change into a market with low leverage. What tends to happen is that oddly enough, market X will tend to start acting like market Y, and market Y will start to act like market Z. If a trader has thoroughly optimized his system to trade market X, he will be in trouble when it starts to trade like market Y.

This is a common problem with many trading systems, especially stock index trading system that tend to be optimized to only one market. Despite the occasionally impressive looking results of these trading systems, there is a poisonous strain in their mix. Now contrast the previous scenario with one in which the trading systems design works well with almost all the markets, A through Z. In this case it does not matter if market Z starts to act like market Y, or if market A starts to act like market P The markets can change as many times as they please and it will not affect performance because the trading systems design is universally robust and it can deal with nearly ALL the various types of markets. Once again, even if the characteristics of the market reshuffle countless times, the system acts as a Swiss army knife easily dealing with any scenario.

Good trading systems are universally robust. As markets and conditions change, these systems are able to deal with the various types of changing market characteristics.

Commodity trading carries risks and is not suitable for all investors. Past performance is not indicative of future performance.

 

To the new futures trading system developer one of the most exciting things to play with is optimization. Optimization is using the power of the PC to look at each possible sequence of parameters and rules, and then using only those rules and / or parameters that have worked the very best. With enough PC crunching power, it is possible to find futures trading systems that completely predicted the past! We can run number crunching PC's on automated routines and have them research many billions of bits of info even while we are sleeping. Many traders do this long enough and later "discover" the holy grail of futures trading systems. They hop straight into the markets with their new super predictive procedures only to find they fall apart in real trading!

What happened? they ask. The answer's that what they created was likely a commodity trading system that was a statistical coincidence (known as a "curve fit”). Curve fitting is where you force a trading system to conform to historic data. The difficulty is that the markets will behave much differently moving forward; therefore, a “perfect” trading system may be rendered worthless. For example, your computer finds the perfect dates historically to have bought and then sold the market. These dates are likely coincidental and have no future value yet sometimes people will base a commodity trading system on them. This is a clear example; nevertheless most curve fits are some complex form of this basic concept.

Let's look at another flawed example. Presume we wanted to optimize nickels that were most inclined to land on heads. What we could do is flip millions of nickels and only select those that landed on heads. Then, we can take those remaining nickels and flip them again, once more only choosing the ones that land on heads. We could repeat this process repeatedly, every time only choosing those nickels that land on heads. At about that point, we would conclude that we had narrowed down our nickels to only a tiny handful that were optimized to land on heads. We could then go out and wager large gambles with those nickels putting all our cash on heads. We'd quickly make a fortune, right? WRONG!

We would quickly lose our money. Those nickels were not optimized for heads; they always had 50 / 50 odds. What might have confused some is that they thought they'd found predictable nickels. All they found was a probabilistic coincidence!

As there is so much data, and so much computing power available, these sorts of mistakes find their way into commodity trading systems all of the time. When developing a commodity trading system it is vital to avoid optimizing as much as practical. You need to find NON curve-fit, robust trading systems. There can be a place for some sorts of optimizing, but it must be handled in the right way.

Commodity trading carries risks and is not suitable for all investors. Past performance is not indicative of future results.